PhD Chapter 2
Results
This series of files compile all analyses done during Chapter 2.
All analyses have been done with R 4.0.2.
Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it
1. Maps
1.1. General map
Stations considered for this Chapter:
1.2. Parameters maps
Maps of functional traits density:
Body: non-calcified tissue
Body: calcareous
Body: calcium carbonate
Body: amorphous calcium carbonate
Body: aragonite
Body: calcite
Body: high magnesium calcite
Body: chitinous
Size: small
Size: medium
Size: large
Food: filter feeders
Food: surface deposit feeders
Food: subsurface deposit feeders
Food: grazers
Food: predators
Food: scavengers
Food: parasites
Mobility: sessile
Mobility: limited
Mobility: mobile
Lifestyle: fixed
Lifestyle: tubicolous
Lifestyle: burrower
Lifestyle: crawler
Lifestyle: swimmer
2. Exploration plots
2.1. Rank-Frequency diagrams
We drew Rank-Frequency diagrams to study the structure of communities when considering frequencies of taxa.
2.2. Abundance-Biomass curves
We drew Abundance-Biomass curves to study the structure of communities when considering density and biomass together.
3. Indicators of ecosystem status
This section tests different indicators to reflect the environmental status in Baie des Sept Îles. We have considered classic methods, such as community characteristics, with functional diversity indices and other techniques. We will look at their results critically to see which could be the best for which situation.
Indices have been grouped based on Salas et al. 2006 (Ocean and Coastal Management).
3.1. Based on species abundance and biomass
3.1.1. Total density & biomass
We calculated a basic community characteristic, the total density of individuals, to see if patterns could be detected in the study area. The same calculation as for Chapter 1 has been performed for the considered stations.
When we considered the data without a distinction by station, global total density is 10,915 individuals.grab-1.
3.1.2. Total biomass
We calculated a basic community characteristic, the total biomass of individuals, to see if patterns could be detected in the study area.
When we considered the data without a distinction by station, global total biomass is 936.6919 gWM.grab-1.
3.1.3. W statistic
This indicator is based on abundance and biomass ranked values, as presented by Warwick (1986) and Clarke (1990). In addition to Abundance-Biomass Curves (see above), it allows to present a disturbed state thanks to a certain structure of the community.
The W statistic is continuous between -1 and 1, and is calculated using this equation:
\[ W = \frac{\sum_{i = 1}^{S}(B_{i} - A_{i})}{50(S - 1)} \]
- \(B_{i}\) is the biomass of a species
- \(A_{i}\) is the abundance of a species
- \(S\) is the specific richness
- \(i\) is a species
When we considered the data without a distinction by station, global W statistic is 0.1035.
3.2. Based on diversity values
3.2.1. Specific richness
We calculated a basic community characteristic, the specific richness, to see if patterns could be detected in the study area. The same calculation as for Chapter 1 has been performed for the considered stations.
ASSUMPTION: A higher richness indicates a high status without perturbation.
When we considered the data without a distinction by station, the global specific richness is 132.
3.2.2. Shannon index
We calculated basic a community characteristic, the Shannon index, to see if patterns could be detected in the study area. The same calculation as for Chapter 1 has been performed for the considered stations.
ASSUMPTION: A higher index indicates a high status without perturbation.
When we considered the data without a distinction by station, the global Shannon index is 3.251545.
3.2.3. Margalef index
We calculated a basic community characteristics, the Margalef index, to see if patterns could be detected in the study area.
ASSUMPTION: A higher index indicates a high status without perturbation. (To check)
When we considered the data without a distinction by station, the global Margalef index is 14.09715.
3.2.4. Pielou evenness
We calculated a basic community characteristics, the Pielou evenness, to see if patterns could be detected in the study area. The same calculation as for Chapter 1 has been performed for the considered stations.
When we considered the data without a distinction by station, the global Pielou evenness is 0.6659178.
3.2.5. Taxonomic diversity
We calculated a basic community characteristic, the taxonomic diversity, to see if patterns could be detected in the study area. The same calculations as for Chapter 1 have been performed for the considered stations.
When we considered the data without a distinction by station, the global taxonomic diversity is 74.16541.
3.3. Based on ecological strategies
3.3.1. Functional diversity
We studied functional diversity based on five biological traits and 26 modalities:
- body composition: non calcified tissue, calcareous, calcareous calcium carbonate, calcareous amorphous calcium carbonate, calcareous aragonite, calcareous calcite, calcareous high magnesium calcite, chitinous
- body size: small, medium, large
- food diet: filter, surface deposit, subsurface deposit, predator, scavenger, grazer, parasite
- mobility: sessile, limited, mobile
- lifestyle: fixed, tubicolous, burrower, crawler, swimmer
Species were assigned a value for each modality using a scale varying from 0 (absence of the modality) to 1 (presence). All where the sum of the values for every modality of a trait equals 1. This allowed to calculate functional richness, evenness and divergence according to Laliberté & Legendre (2010).
For some reason, R is not able to calculate a global value…
3.3.2. Benthic opportunistic polychaete/amphipod ratio (BOPA)
BOPA is an index that uses a relative abundance ratio of species in a community to infer a state of perturbation. Ratios with many species have been tested, and opportunistic polychaetes and amphipods have been selected to be the most pertinent (originally to detect effects of an oil-spill on soft-bottom communities, e.g. from the Sea Empress or the Amoco Cadiz). It has been updated from its original form in 2000.
BOPA is continuous between 0 and \(log_{10}(2)\) (~ 0.3), and is calculated using this equation:
\[ BOPA = \left( \frac{f_{P}}{f_{A} + 1} + 1 \right) \]
- \(f_{P}\) is the relative frequency of opportunistic polychaetes (abundance / total density)
- \(f_{A}\) is the relative frequency of amphipods (abundance / total density)
We considered ecological groups GIII to GV for polychaetes and GI for amphipods (without Jassa genera, see AMBI section below).
ASSUMPTION: Dominance of amphipods characterizes pristine ecosystems, while a dominance of opportunistic polychaetes indicates a perturbed state.
These are the polychaetes and amphipods present in our species list (including the confidence score used during group classification).
| taxon_name | group | source | confidence_score |
|---|---|---|---|
| cossura_longocirrata | IV | AMBI list | 3 |
| eteone_sp | III | AMBI list | 2 |
| hediste_diversicolor | III | AMBI list | 3 |
| praxillella_praetermissa | III | AMBI list | 3 |
| taxon_name | group | source | confidence_score |
|---|---|---|---|
| ameroculodes_edwardsi | I | AMBI list | 3 |
| ampelisca_vadorum | I | AMBI list | 3 |
| byblis_gaimardii | I | AMBI list | 3 |
| lysianassidae_spp | I | AMBI list | 2 |
| maera_danae | I | AMBI list (Maera sp.) | 2 |
| phoxocephalus_holbolli | I | AMBI list | 3 |
| pontoporeia_femorata | I | AMBI list | 3 |
| quasimelita_formosa | I | AMBI list (MELITIDAE) | 2 |
| quasimelita_quadrispinosa | I | AMBI list | 3 |
When we considered the data without a distinction by station, the global BOPA index is 0.003443646.
3.3.3. BenthoVal index
This index is a work-in-progress by the team of Céline Labrune at IFREMER. This pressure score still needs to be enhanced so that more human activities are included and the score is better defined.
3.4. Based on characteristic species
3.4.1. AZTI Marine Biotic Index (AMBI)
AMBI (initially named the biotic coefficient) is an ecological index that is used to detect a perturbation in an ecosystem based on the composition of the communities (Borja et al., 2000). This perturbation is linked with an organic matter increase, according to Pearson and Rosenberg (1978) model.
To compute the index, species are classed into five groups in relation to their tolerance to this perturbation:
- group I (GI): vulnerable species
- group II (GII): indifferent species
- group III (GIII): tolerant species
- group IV (GIV): first-order opportunistics
- group V (GV): second-order opportunistics
These groups are based on expert opinion on the physiology of species and experimental studies, but the attribution of a species to a group can be somewhat arbitrary (e.g. based on related phyla information) so it needs to be interpretated carefully.
AMBI is continuous between 0 to 6 (7 when the habitat is azoic, thus considered extremely disturbed), and is calculated using this equation in a dedicated software:
\[ AMBI = \frac{\sum_{i}^{GI-V} w_{i} . P_{i}}{100} \]
- \(P_{i}\) is the proportion of each group (percentage of the total density of species)
- \(w_{i}\) is the weighting parameter of each group (respectively 0, 1.5, 3, 4.5 and 6)
- \(i\) is the ecological group
ASSUMPTION: Sensitive species are only present in pristine ecosystems, while the dominance of opportunists indicates a perturbed state.
Here is the classification of the sampled taxa in ecological groups, adapted from Borja et al. (2000):
| taxon_name | group | source | confidence_score |
|---|---|---|---|
| aceroides_aceroides_latipes | II | AMBI list | 3 |
| akanthophoreus_gracilis | I | AMBI list | 3 |
| ameritella_agilis | II | AMBI list | 3 |
| ameroculodes_edwardsi | I | AMBI list | 3 |
| ampelisca_vadorum | I | AMBI list | 3 |
| amphipoda | not_assigned | 0 | |
| anonyx_lilljeborgi | II | AMBI list | 3 |
| anthozoa | II | AMBI list | 1 |
| arcteobia_anticostiensis | II | AMBI list (POLYNOIDAE) | 2 |
| arrhoges_occidentalis | I | AMBI list (Aporrhais sp.) | 2 |
| astarte_sp | I | AMBI list | 2 |
| axinopsida_orbiculata | III | AMBI list | 3 |
| axiothella_catenata | I | AMBI list (Axiothella sp.) | 2 |
| bathymedon_longimanus | II | AMBI list | 3 |
| bathymedon_obtusifrons | II | AMBI list | 3 |
| bipalponephtys_neotena | II | AMBI list | 3 |
| boreochiton_ruber | I | AMBI list | 3 |
| brachydiastylis_sp | II | AMBI list | 2 |
| byblis_gaimardii | I | AMBI list | 3 |
| cancer_irroratus | II | Gittenberg & Van Loon, 2013 (Cancer pagurus) | 1 |
| caprella_septentrionalis | II | AMBI list | 3 |
| chaetodermatida | not_assigned | 0 | |
| chionoecetes_opilio | I | AMBI list | 3 |
| chlamys_islandica | I | AMBI list | 3 |
| chone_sp | II | AMBI list | 2 |
| ciliatocardium_ciliatum | I | AMBI list | 3 |
| cirripedia | II | AMBI list | 3 |
| cistenides_granulata | II | AMBI list | 3 |
| cossura_longocirrata | IV | AMBI list | 3 |
| crassicorophium_bonellii | III | AMBI list | 3 |
| crenella_decussata | I | AMBI list | 3 |
| cumacea | I | AMBI list | 3 |
| cyclocardia_borealis | I | AMBI list (Cyclocardia thouarsii) | 2 |
| cyrtodaria_siliqua | I | Gilkinson et al., 2005 | 2 |
| diastylis_rathkei | III | AMBI list | 3 |
| diastylis_sculpta | II | AMBI list | 3 |
| diastylis_sp | I | AMBI list | 1 |
| echinarachnius_parma | I | AMBI list (ECHINOIDEA) | 2 |
| edotia_montosa | II | AMBI list | 3 |
| ennucula_tenuis | II | AMBI list | 3 |
| eteone_sp | III | AMBI list | 2 |
| euchone_sp | II | AMBI list | 2 |
| eudorella_emarginata | II | AMBI list | 3 |
| eudorellopsis_integra | II | Tillin & Tyler-Walters, 2014 (group of Bathyporeia elegans & Eudorellopsis deformis) | 2 |
| euspira_pallida | II | AMBI list | 3 |
| glycera_capitata | II | AMBI list | 3 |
| glycera_sp | II | AMBI list | 2 |
| goniada_maculata | II | AMBI list | 3 |
| guernea_prinassus_nordenskioldi | III | de la Ossa Carretero et al., 2011 (Dexamene spinosa) | 1 |
| halacaridae_spp | I | AMBI list | 2 |
| haminoea_solitaria | II | AMBI list | 3 |
| hardametopa_carinata | II | AMBI list (STENOTHOIDAE) | 1 |
| harmothoe_sp | II | AMBI list | 2 |
| harpacticoida | not_assigned | 0 | |
| hediste_diversicolor | III | AMBI list | 3 |
| heteranomia_squamula | I | AMBI list | 3 |
| hiatella_arctica | I | AMBI list | 3 |
| holothuroidea | I | AMBI list | 3 |
| idotea_phosphorea | II | AMBI list (Idotea sp.) | 2 |
| ischyroceridae_spp | II | AMBI list (Ischyrocerus anguipes) | 2 |
| ischyrocerus_anguipes | II | AMBI list | 3 |
| isopoda | not_assigned | 0 | |
| lacuna_vincta | II | AMBI list | 3 |
| lamprops_fuscatus | I | AMBI list | 3 |
| lamprops_quadriplicata | I | AMBI list | 3 |
| lepeta_caeca | I | AMBI list | 3 |
| leucon_leucon_nasicoides | II | AMBI list | 3 |
| littorina_littorea | II | AMBI list | 3 |
| lumbrineridae_spp | II | AMBI list | 2 |
| lysianassidae_spp | I | AMBI list | 2 |
| macoma_calcarea | II | AMBI list | 3 |
| maera_danae | I | AMBI list (Maera sp.) | 2 |
| maldane_sarsi | II | AMBI list | 3 |
| maldanidae_spp | I | AMBI list | 2 |
| monoculopsis_longicornis | II | AMBI list | 3 |
| muculus_musculus_discors | I | AMBI list | 3 |
| mytilus_sp | III | AMBI list | 2 |
| nematoda | III | AMBI list | 1 |
| nemertea | III | AMBI list | 1 |
| neoleanira_tetragona | II | AMBI list | 3 |
| nephtyidae_spp | II | AMBI list | 2 |
| nephtys_caeca | II | AMBI list | 3 |
| nephtys_incisa | II | AMBI list | 3 |
| nephtys_sp | II | AMBI list | 2 |
| nuculana_minuta | I | AMBI list | 3 |
| nymphonidae_spp | not_assigned | 0 | |
| oenopota_sp | I | AMBI list | 2 |
| oligochaeta | V | AMBI list | 1 |
| ophelia_limacina | I | AMBI list | 3 |
| opheliidae_spp | I | AMBI list (Ophelia limacina) | 2 |
| ophiopholis_aculeata | II | AMBI list | 3 |
| ophiura_robusta | II | AMBI list | 3 |
| orchomenella_minuta | II | AMBI list | 3 |
| ostracoda | I | Bodegart et al., 1997 ; Ruiz et al., 2005 ; Gooday et al., 2009 | 1 |
| pagurus_pubescens | II | AMBI list | 3 |
| pagurus_sp | II | AMBI list | 2 |
| pandalus_montagui | II | AMBI list | 3 |
| parathyasira_equalis | III | AMBI list | 3 |
| parvicardium_pinnulatum | I | AMBI list | 3 |
| periploma_leanum | II | AMBI list (Periploma discus) | 2 |
| philine_lima | II | AMBI list | 3 |
| philomedes_sp | II | AMBI list | 3 |
| pholoe_longa | II | AMBI list (Pholoe sp.) | 2 |
| pholoe_sp | II | AMBI list | 2 |
| phoxocephalus_holbolli | I | AMBI list | 3 |
| polynoidae_spp | II | AMBI list (POLYNOIDAE) | 2 |
| pontogeneia_inermis | II | AMBI list (Pontogeneia rostrata) | 2 |
| pontoporeia_femorata | I | AMBI list | 3 |
| praxillella_praetermissa | III | AMBI list | 3 |
| propebela_turricula | I | AMBI list | 3 |
| protomedeia_fasciata | II | AMBI list | 3 |
| protomedeia_grandimana | II | AMBI list | 3 |
| puncturella_noachina | I | AMBI list | 3 |
| quasimelita_formosa | I | AMBI list (MELITIDAE) | 2 |
| quasimelita_quadrispinosa | I | AMBI list | 3 |
| retusa_obtusa | II | AMBI list | 3 |
| sabellidae_spp | I | AMBI list | 2 |
| scoletoma_fragilis | II | AMBI list | 3 |
| scoletoma_sp | II | AMBI list | 2 |
| scoloplos_sp | I | AMBI list | 2 |
| serripes_groenlandicus | I | AMBI list | 3 |
| sipuncula | I | AMBI list | 1 |
| solamen_glandula | II | AMBI list (Solamen columbianum) | 2 |
| solariella_sp | I | AMBI list | 2 |
| strongylocentrotus_sp | I | AMBI list (Strongylocentrotus droebachiensis) | 3 |
| tachyrhynchus_erosus | I | AMBI list (Turritella sp.) | 2 |
| thracia_septentrionalis | I | AMBI list | 3 |
| thyasira_gouldi | I | AMBI list | 3 |
| thyasira_sp | II | AMBI list | 1 |
| trichotropis_bicarinata | II | AMBI list (Euspira sp.) | 2 |
| turritellopsis_stimpsoni | I | AMBI list (Turritella sp.) | 2 |
| yoldia_myalis | I | AMBI list (Yoldia limatula) | 2 |
When we considered the data without a distinction by station, the global AMBI index is 1.543.
3.4.2. Multivariate AMBI (M-AMBI)
M-AMBI is a complementary method to AMBI that is based on a multivariate ordination (factorial analysis and discriminant analysis) of the stations using the species richness, the Shannon index and the AMBI index (Muxika et al. 2007). This index needs to explicitely define reference conditions, corresponding to ‘bad’ and ‘high’ conditions.
These values are called ‘references’, but this vision can be biaised. We have calculated them using the 5 % and 95 % percentiles of the distribution. This is a recommendation by Nicolas Desroy, so that we do not detect an modification of quality status when there is a small perturbation (see work by Pearson & Rosenberg and the Intermediate Disturbance Hypothesis).
This calculation provided S = 5, H = 0.98, AMBI = 0.75 for ‘bad’ conditions and S = 21, H = 2.53, AMBI = 2.52 for ‘high’ conditions.
M-AMBI is continuous between 0 and 1, and is calculated using a dedicated software.
ASSUMPTION: A high richness, high diversity and low AMBI index indicate a high status without perturbation.
As we do not historical data or other reference systems to produce relevant reference conditions, it is difficult to calculate a global M-AMBI here.
No clear tendancy can be discovered here, apart from the fact that the overall status seems to be ‘High’. Several hypothesises can explain this result:
- the M-AMBI index describes reality well, so that overall perturbation from organic matter is low
- there is a bias in the index due to the species classification in groups, originally suited for European ecosystems
- the assumptions for the reference values are not correct
- the configuration of the bay makes the perturbation small relative to the water volume and bathymetric condition
Further work is needed to determine the individual responses of somes species, along with the use of different methods to understand other perturbations and cumulative impacts.
3.4.3. BENTIX
BENTIX is an index based on the same theory as the AMBI, where species are placed in groups based on their tolerance to perturbation (Simboura & Zenetos, 2002). Here also, this perturbation is principally linked to organic matter increase, but two groups only are present:
- GS: species that are sensitive or indifferent to a perturbation (~ AMBI groups I and II)
- GT: species that are tolerant to a perturbation and opportunists (~ AMBI groups III to V)
BENTIX is continuous between 2 and 6 (0 when the habitat is azoic, thus considered extremely disturbed), and is calculated using this equation:
\[ BENTIX = \frac{(6 . P_{GS}) + (2 . P_{GT})}{100} \]
- \(P_{GS}\) is the proportion of sensitive species (percentage of the total density of species)
- \(P_{GT}\) is the proportion of tolerant species (percentage of the total density of species)
ASSUMPTION: Sensitive species are only present in pristine ecosystems, while the dominance of opportunists indicates a perturbed state.
Here is the classification of the sampled taxa in ecological groups, adapted from Simboura & Zenetos (2002):
| taxon_name | group | source | confidence_score |
|---|---|---|---|
| aceroides_aceroides_latipes | S | AMBI list | 3 |
| akanthophoreus_gracilis | S | AMBI list | 3 |
| ameritella_agilis | S | AMBI list | 3 |
| ameroculodes_edwardsi | S | AMBI list | 3 |
| ampelisca_vadorum | S | AMBI list | 3 |
| amphipoda | not_assigned | 0 | |
| anonyx_lilljeborgi | S | AMBI list | 3 |
| anthozoa | S | AMBI list | 1 |
| arcteobia_anticostiensis | S | AMBI list (POLYNOIDAE) | 2 |
| arrhoges_occidentalis | S | AMBI list (Aporrhais sp.) | 2 |
| astarte_sp | S | AMBI list | 2 |
| axinopsida_orbiculata | T | AMBI list | 3 |
| axiothella_catenata | S | AMBI list (Axiothella sp.) | 2 |
| bathymedon_longimanus | S | AMBI list | 3 |
| bathymedon_obtusifrons | S | AMBI list | 3 |
| bipalponephtys_neotena | S | AMBI list | 3 |
| boreochiton_ruber | S | AMBI list | 3 |
| brachydiastylis_sp | S | AMBI list | 2 |
| byblis_gaimardii | S | AMBI list | 3 |
| cancer_irroratus | S | Gittenberg & Van Loon, 2013 (Cancer pagurus) | 1 |
| caprella_septentrionalis | S | AMBI list | 3 |
| chaetodermatida | not_assigned | 0 | |
| chionoecetes_opilio | S | AMBI list | 3 |
| chlamys_islandica | S | AMBI list | 3 |
| chone_sp | S | AMBI list | 2 |
| ciliatocardium_ciliatum | S | AMBI list | 3 |
| cirripedia | S | AMBI list | 3 |
| cistenides_granulata | S | AMBI list | 3 |
| cossura_longocirrata | T | AMBI list | 3 |
| crassicorophium_bonellii | T | AMBI list | 3 |
| crenella_decussata | S | AMBI list | 3 |
| cumacea | S | AMBI list | 3 |
| cyclocardia_borealis | S | AMBI list (Cyclocardia thouarsii) | 2 |
| cyrtodaria_siliqua | S | Gilkinson et al., 2005 | 2 |
| diastylis_rathkei | T | AMBI list | 3 |
| diastylis_sculpta | S | AMBI list | 3 |
| diastylis_sp | S | AMBI list | 1 |
| echinarachnius_parma | S | AMBI list (ECHINOIDEA) | 2 |
| edotia_montosa | S | AMBI list | 3 |
| ennucula_tenuis | S | AMBI list | 3 |
| eteone_sp | T | AMBI list | 2 |
| euchone_sp | S | AMBI list | 2 |
| eudorella_emarginata | S | AMBI list | 3 |
| eudorellopsis_integra | S | Tillin & Tyler-Walters, 2014 (group of Bathyporeia elegans & Eudorellopsis deformis) | 2 |
| euspira_pallida | S | AMBI list | 3 |
| glycera_capitata | S | AMBI list | 3 |
| glycera_sp | S | AMBI list | 2 |
| goniada_maculata | S | AMBI list | 3 |
| guernea_prinassus_nordenskioldi | T | de la Ossa Carretero et al., 2011 (Dexamene spinosa) | 1 |
| halacaridae_spp | S | AMBI list | 2 |
| haminoea_solitaria | S | AMBI list | 3 |
| hardametopa_carinata | S | AMBI list (STENOTHOIDAE) | 1 |
| harmothoe_sp | S | AMBI list | 2 |
| harpacticoida | not_assigned | 0 | |
| hediste_diversicolor | T | AMBI list | 3 |
| heteranomia_squamula | S | AMBI list | 3 |
| hiatella_arctica | S | AMBI list | 3 |
| holothuroidea | S | AMBI list | 3 |
| idotea_phosphorea | S | AMBI list (Idotea sp.) | 2 |
| ischyroceridae_spp | S | AMBI list (Ischyrocerus anguipes) | 2 |
| ischyrocerus_anguipes | S | AMBI list | 3 |
| isopoda | not_assigned | 0 | |
| lacuna_vincta | S | AMBI list | 3 |
| lamprops_fuscatus | S | AMBI list | 3 |
| lamprops_quadriplicata | S | AMBI list | 3 |
| lepeta_caeca | S | AMBI list | 3 |
| leucon_leucon_nasicoides | S | AMBI list | 3 |
| littorina_littorea | S | AMBI list | 3 |
| lumbrineridae_spp | S | AMBI list | 2 |
| lysianassidae_spp | S | AMBI list | 2 |
| macoma_calcarea | S | AMBI list | 3 |
| maera_danae | S | AMBI list (Maera sp.) | 2 |
| maldane_sarsi | S | AMBI list | 3 |
| maldanidae_spp | S | AMBI list | 2 |
| monoculopsis_longicornis | S | AMBI list | 3 |
| muculus_musculus_discors | S | AMBI list | 3 |
| mytilus_sp | T | AMBI list | 2 |
| nematoda | T | AMBI list | 1 |
| nemertea | T | AMBI list | 1 |
| neoleanira_tetragona | S | AMBI list | 3 |
| nephtyidae_spp | S | AMBI list | 2 |
| nephtys_caeca | S | AMBI list | 3 |
| nephtys_incisa | S | AMBI list | 3 |
| nephtys_sp | S | AMBI list | 2 |
| nuculana_minuta | S | AMBI list | 3 |
| nymphonidae_spp | not_assigned | 0 | |
| oenopota_sp | S | AMBI list | 2 |
| oligochaeta | T | AMBI list | 1 |
| ophelia_limacina | S | AMBI list | 3 |
| opheliidae_spp | S | AMBI list (Ophelia limacina) | 2 |
| ophiopholis_aculeata | S | AMBI list | 3 |
| ophiura_robusta | S | AMBI list | 3 |
| orchomenella_minuta | S | AMBI list | 3 |
| ostracoda | S | Bodegart et al., 1997 ; Ruiz et al., 2005 ; Gooday et al., 2009 | 1 |
| pagurus_pubescens | S | AMBI list | 3 |
| pagurus_sp | S | AMBI list | 2 |
| pandalus_montagui | S | AMBI list | 3 |
| parathyasira_equalis | T | AMBI list | 3 |
| parvicardium_pinnulatum | S | AMBI list | 3 |
| periploma_leanum | S | AMBI list (Periploma discus) | 2 |
| philine_lima | S | AMBI list | 3 |
| philomedes_sp | S | AMBI list | 3 |
| pholoe_longa | S | AMBI list (Pholoe sp.) | 2 |
| pholoe_sp | S | AMBI list | 2 |
| phoxocephalus_holbolli | S | AMBI list | 3 |
| polynoidae_spp | S | AMBI list (POLYNOIDAE) | 2 |
| pontogeneia_inermis | S | AMBI list (Pontogeneia rostrata) | 2 |
| pontoporeia_femorata | S | AMBI list | 3 |
| praxillella_praetermissa | T | AMBI list | 3 |
| propebela_turricula | S | AMBI list | 3 |
| protomedeia_fasciata | S | AMBI list | 3 |
| protomedeia_grandimana | S | AMBI list | 3 |
| puncturella_noachina | S | AMBI list | 3 |
| quasimelita_formosa | S | AMBI list (MELITIDAE) | 2 |
| quasimelita_quadrispinosa | S | AMBI list | 3 |
| retusa_obtusa | S | AMBI list | 3 |
| sabellidae_spp | S | AMBI list | 2 |
| scoletoma_fragilis | S | AMBI list | 3 |
| scoletoma_sp | S | AMBI list | 2 |
| scoloplos_sp | S | AMBI list | 2 |
| serripes_groenlandicus | S | AMBI list | 3 |
| sipuncula | S | AMBI list | 1 |
| solamen_glandula | S | AMBI list (Solamen columbianum) | 2 |
| solariella_sp | S | AMBI list | 2 |
| strongylocentrotus_sp | S | AMBI list (Strongylocentrotus droebachiensis) | 3 |
| tachyrhynchus_erosus | S | AMBI list (Turritella sp.) | 2 |
| thracia_septentrionalis | S | AMBI list | 3 |
| thyasira_gouldi | S | AMBI list | 3 |
| thyasira_sp | S | AMBI list | 1 |
| trichotropis_bicarinata | S | AMBI list (Euspira sp.) | 2 |
| turritellopsis_stimpsoni | S | AMBI list (Turritella sp.) | 2 |
| yoldia_myalis | S | AMBI list (Yoldia limatula) | 2 |
When we considered the data without a distinction by station, the global BENTIX index is 5.213659.
4. Ecological Quality Status
When relevant, we calculated an Ecological Quality Ratio (EQR) as established by the WFD and MSFD (which varies between 0 and 1). This ratio is calculated with the folowwing equation:
\[ EQR = \frac{V_{ind} - Ref_{bad}}{Ref_{good} - Ref_{bad}} \]
- \(V_{ind}\) is the value of an indicator at a certain location
- \(Ref_{bad}\) is the reference value for a “bad” status
- \(Ref_{good}\) is the reference value for a “good” status
This ratio is then classed into Ecological Quality Status categories, where reference values and limits for class transitions are specific to each indicator. Five classes are typically described:
- bad (red #FF0000)
- poor (orange #FFA500)
- moderate (yellow #EEEE00)
- good (green #228B22)
- high (blue #0000EE)
We calculated this ratio using different indicators, in order to compare their efficiency and relevance.
Specific richness
We defined class thresholds with 20 %, 40 %, 60 and 80 % of the maximal specific richness.
Shannon index
We defined class thresholds with 20 %, 40 %, 60 and 80 % of the maximal Shannon index.
Margalef index
We defined class thresholds with 20 %, 40 %, 60 and 80 % of the maximal Margalef index.
Simpson index
We defined class thresholds with 20 %, 40 %, 60 and 80 % of the maximal Simpson index.
BOPA
We defined class thresholds using the method from Dauvin & Ruellet (2007).
AMBI
We defined class thresholds using the methods from Borja et al. (2000) and Muxika et al. (2005).
M-AMBI
We defined class thresholds using the method from Muxika et al. (2007).
BENTIX
We defined class thresholds using the method from Simboura & Zenetos (2002).
5. Relationships between indicators and abiotic parameters
In this section, we study the statistical relationships between indicators calculated above and different abiotic parameters, in order to understand how well they can be used to detect perturbations.
5.1. Covariation
Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.
⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.
Specific richness
Total density
Total biomass
Shannon index
Margalef index
Simpson index
Pielou evenness
W statistic
Taxonomic diversity
Functional richness
Functional evenness
Functional divergence
AMBI
M_AMBI
BOPA
BENTIX
5.2. Correlation
Correlations have been calculated with Spearman’s rank coefficients.
Quitting from lines 777-788 (C2_analyses.Rmd) Error in pandoc.table.return(…) : Wrong number of parameters (14 instead of 17) passed: justify De plus : There were 50 or more warnings (use warnings() to see the first 50)
| S | N | B | H | margalef | lambda | J | W | delta | FR | FE | FD | AMBI | M_AMBI | BOPA | BENTIX | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| om | 0.7858 | 0.2113 | 0.9048 | 0.2093 | 0.7012 | 0.1701 | 0.1614 | 0.5035 | 0.544 | 0.2628 | 0.7284 | 0.4963 | 0.08394 | 0.3319 | 0.05618 | 0.001327 |
| gravel | 0.7662 | 0.9425 | 0.1117 | 0.9048 | 0.7948 | 0.6896 | 0.4692 | 0.1512 | 0.4459 | 0.01174 | 0.4148 | 0.1513 | 0.5795 | 0.9576 | 0.8576 | 0.07776 |
| sand | 0.5414 | 0.3846 | 0.5339 | 0.752 | 0.7485 | 0.7805 | 0.7828 | 0.6851 | 0.4045 | 0.4099 | 0.6168 | 0.4429 | 0.03891 | 0.8558 | 0.00334 | 0.001331 |
| silt | 0.581 | 0.8963 | 0.7923 | 0.4834 | 0.5607 | 0.3744 | 0.5692 | 0.3672 | 0.1509 | 0.2264 | 0.3508 | 0.9844 | 0.07875 | 0.9425 | 0.001537 | 0.003456 |
| clay | 0.3134 | 0.4336 | 0.8338 | 0.6486 | 0.3749 | 0.7849 | 0.8392 | 0.7052 | 0.5453 | 0.7737 | 0.4939 | 0.4117 | 0.6054 | 0.5407 | 0.4685 | 0.8405 |
| arsenic | 0.005376 | 0.1238 | 0.08845 | 0.04503 | 0.01607 | 0.1243 | 0.9356 | 0.03544 | 0.1991 | 0.003949 | 0.1586 | 0.8603 | 0.7147 | 0.04233 | 0.005411 | 0.1592 |
| cadmium | 0.001171 | 0.6656 | 0.07386 | 0.002263 | 0.0006803 | 0.008567 | 0.1701 | 0.0003388 | 0.00396 | 0.002036 | 0.01018 | 0.09262 | 0.8475 | 0.003504 | 0.01348 | 0.3893 |
| chromium | 0.0004702 | 0.08459 | 0.1476 | 0.004269 | 0.001052 | 0.01769 | 0.6766 | 0.01158 | 0.02095 | 0.0001797 | 0.07347 | 0.5643 | 0.8322 | 0.003027 | 0.002598 | 0.09147 |
| copper | 0.001731 | 0.07495 | 0.1337 | 0.0195 | 0.003892 | 0.05222 | 0.8009 | 0.04442 | 0.03895 | 0.0003739 | 0.07738 | 0.2608 | 0.8572 | 0.007774 | 0.008463 | 0.05769 |
| iron | 5.82e-05 | 0.004321 | 0.6322 | 0.00892 | 0.0006646 | 0.03854 | 0.725 | 0.06932 | 0.07715 | 3.87e-05 | 0.2485 | 0.5548 | 0.9679 | 0.001522 | 0.009583 | 0.3544 |
| manganese | 0.00256 | 0.3224 | 0.3861 | 0.006285 | 0.001592 | 0.01793 | 0.3814 | 0.01201 | 0.01088 | 0.0009191 | 0.01642 | 0.4765 | 0.7532 | 0.008646 | 0.0004345 | 0.09382 |
| mercury | 0.0146 | 0.3891 | 0.8673 | 0.03882 | 0.0135 | 0.07408 | 0.4404 | 0.08806 | 0.01764 | 0.001231 | 0.04375 | 0.1336 | 0.6622 | 0.05723 | 0.004795 | 0.07972 |
| lead | 0.001371 | 0.1643 | 0.109 | 0.00859 | 0.002352 | 0.02692 | 0.5997 | 0.01223 | 0.02485 | 0.00101 | 0.04112 | 0.3171 | 0.9397 | 0.005709 | 0.001546 | 0.2018 |
| zinc | 0.0007423 | 0.1333 | 0.08864 | 0.00819 | 0.001442 | 0.02772 | 0.5628 | 0.008563 | 0.02152 | 0.0003788 | 0.0519 | 0.09615 | 0.9183 | 0.004098 | 0.00654 | 0.1206 |